Systematic assessment of inflammation by magnetic resonance imaging in the posterior elements of the spine in ankylosing spondylitis
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is sensitive for scoring inflammatory lesions in the spine, but attention has primarily focused on vertebral bodies, and no study has systematically examined the posterior elements. We aimed to systematically determine the frequency and distribution of inflammatory changes in the posterior elements of the spine using MRI, and to assess the reliability of their detection and their impact on discrimination of spinal MRI. METHODS: We scanned 32 patients recruited to placebo-controlled trials of anti-tumor necrosis factor therapy. Inflammatory lesions were detected by systematic review of consecutive sagittal STIR slices of the entire spine. Two readers evaluated pretreatment and posttreatment scans, blinded to treatment and time point. Inflammation was scored dichotomously (present/absent) in each posterior structure. Reproducibility was assessed by calculating random model variance components and generalizability coefficients, and discrimination by using Guyatt's effect size. RESULTS: Most patients (87.5%) had > or =1 lesion in the posterior elements (mean +/- SD number of affected spinal levels per patient 6.7 +/- 5.3), and they were detected most frequently in the thoracic spine. Interobserver reproducibility for total lesion count was very good to excellent for lesions in the thoracic spine and transverse and spinous processes. The addition of a simple dichotomous method for scoring posterior element inflammation substantially enhanced the discrimination observed using established MRI methods for scoring vertebral body inflammation. CONCLUSION: Inflammatory lesions in the posterior elements were present in the majority of patients with AS, and standard MRI protocols of the spine should be modified to ensure adequate visualization of posterolateral structures.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".